In the era of data-driven marketing, companies have come to recognize the value of leveraging artificial intelligence (AI) and machine learning (ML) to unlock insights from their first-party data and enhance customer engagement. By applying data science concepts to unified, first-party datasets, marketing, customer experience, digital product, analytics, and other growth-focused teams can gain a deeper understanding of their customers’ preferences, needs, and behaviors, and use those insights to build smarter segments and deliver real-time, personalized experiences that transform customer relationships and unleash growth.
However, in the pursuit of unlocking the potential of AI, many organizations make critical errors that can hinder their progress and undermine the effectiveness of their AI initiatives. Here are some common mistakes when using AI with first-party customer data and how to overcome them.
Insufficient data quality: Before implementing AI algorithms, it is essential to ensure the quality of your first-party data. A clean and reliable dataset is crucial for training accurate AI models and generating meaningful insights, yet many companies fail to invest sufficient time and resources in data cleansing, normalization, and validation. By implementing robust data governance practices, regularly monitoring data sources, and employing data quality frameworks and tools, companies can significantly enhance the quality of the customer data used for AI training and analysis.
Lack of granular data: Related to the aforementioned data quality issue is a lack of highly granular data for the behaviors or actions that companies are trying to predict. For example, when distinguishing between two groups of people (e.g., those highly likely to buy and those that are less likely to buy), a model needs customer attributes that are statistically more common in one group than in the other. The more relevant granular data that is collected, the better the chances of training a good predictive model. It helps if companies have a hypothesis (e.g., what kind of visitor behavior indicates that they are likely to buy something) so they can validate whether or not they have sufficient data.
Lack of clear objectives: Companies often fall into the trap of wasting resources on AI initiatives that don’t align with their business strategies. Therefore, it’s important to identify the desired outcomes and establish key performance indicators (KPIs) before initiating any AI project. For example, are you looking to understand customer intent to maximize engagement and conversion rates? Do you want to deliver personalized product recommendations based on propensity to buy in order to boost customer engagement and loyalty? By clearly defining objectives, companies can align their AI initiatives with strategic goals and ensure a focused approach to extracting actionable insights from their first-party data. Regular evaluation and adjustment of objectives based on evolving business needs are also important.
Inadequate data governance: Data governance plays a vital role in ensuring data privacy, security, and compliance, yet many companies neglect to establish robust data governance frameworks when dealing with their first-party datasets. This can lead to unauthorized access, data breaches, and legal complications. A comprehensive data governance strategy, including consent management mechanisms, data anonymization or pseudonymization techniques, and compliance with privacy regulations, should be implemented to mitigate these risks. Regular audits, employee training, and proactive data security measures can also help ensure that AI-powered first-party data initiatives comply with data governance standards.
Bias in data and algorithms: As previously noted, AI models are only as good as the data they are trained on. Yet companies often overlook the presence of bias in their data, leading to biased AI outcomes. By conducting thorough data audits, employing diverse and representative data samples, and using bias detection and mitigation techniques, companies can proactively identify and address bias in both the data and the algorithms. Regularly testing and auditing AI models for fairness can also help reduce bias.
Inadequate change management: Implementing AI technologies often requires organizational and process changes. For instance, machine learning models have historically required data science skills to get value from them. But technologies designed to harness the power of first-party data, like a customer data platform (CDP), give business users the ability to access and use models like CLV and propensity right out of the box. Therefore, companies need to conduct a core process assessment to ensure their teams are prepared for the changes taking place. Setting clear goals and including all relevant stakeholders from the start can help teams level-set on where they are today and make more deliberate choices about what will work best for their organization going forward.
Neglecting continuous monitoring and evaluation: AI models are not static entities but require continuous monitoring and evaluation to ensure their effectiveness over time. Companies often make the mistake of deploying AI models and leaving them unmonitored. As data patterns, user preferences, and business dynamics evolve, AI models may become outdated or less accurate. Regular monitoring, feedback loops, and periodic model retraining are essential to maintain the relevance and performance of an AI-powered first-party data strategy.
Applying AI to first-party data sets can provide companies with valuable insights and a competitive advantage. However, avoiding common mistakes is crucial to maximize the benefits of AI-powered customer experiences. By recognizing the pitfalls and taking necessary countermeasures, organizations can unlock the transformative power of AI and make data-driven decisions that engage customers and drive growth in the modern business landscape.
This article was originally published in Emerce on July 6, 2023.
